Rotating members generally are supported by a stationary body through bearings where, for example, the outer race of the bearing is permanently affixed to an outer housing and the inner race is permanently affixed to a rotating shaft. Rollers are placed between the inner and outer races and permit, with proper lubrication, the rotating element to turn freely with little restraint. If the rolling element experiences forces which cause it to operate off rotational center, forces are transmitted to the stationary body through the bearings inducing slight oscillatory motion of the outer housing. A vibration sensor, such as an accelerometer, placed on the housing will output a signal linearly related to the oscillatory motion of the normally stationary body. There can be many sources which introduce this oscillatory macroscopic motion of the machine housing. The analysis of this motion is referred to as vibration analysis. It is employed to monitor the status of the rotating equipment to provide early warnings of potential faults. This macroscopic motion is more broadly modal excitation. The excitation sources can be unbalance, misalignment, improper gear meshing, a loose machine relative to its housing, defective bearings, etc.
In addition to the oscillatory motion (vibration) introduced on a macroscopic scale, many faults commonly experienced in bearings, gears, etc., also introduce, on a microscopic scale, stress wave packets which propagate away from the initiation site at the speed of sound in the media (e.g., metal). These stress wave packets are short-term, fractional to a few milliseconds, transient events which accompany events such as metal-to-metal impacting, fatigue cracking, friction, and similar events. The source of the stress wave initiation is generally concentrated in a small region which generates primarily “s” waves. As “s” waves propagate away from the initiation site, they introduce a “ripple” traversing on the surface of the housing. This ripple will induce an output in an accelerometer (which responds to absolute motion) or other sensors such as strain gages, etc. Since an accelerometer is generally the sensor of choice for the macroscopic (vibration) motion monitoring, it is logical to also adopt the accelerometer as the sensor of choice for stress wave analysis.
Appropriate analysis of these stress wave packets provide valuable insight to the presence of mechanical faults as well as assistance in identifying the severity of the fault. Typically, the stress activity is easily separated from the more macroscopic (vibration) motion activity simply by routing the sensor (accelerometer) through a high pass filter to reject the lower frequency vibration driven component of the sensor (accelerometer) output. The all-important feature of “stress wave” analysis is the fact that stress waves are only present if a fault is present (i.e., a type of fault which generates stress waves, of which there are many). By capturing the level (such as peak g-levels) of the stress waves (the peak level must be the peak observed over a sufficient time, e.g., approximately 10+ rotations) and trending, a very reliable indicator for status regarding health of the rotating machinery is provided. This is especially true for rotating roller bearings.
The invention of this patent detects the presence of stress waves and outputs the peak g-level of the stress wave activity which occurs over a pre-set time period. In a preferred embodiment, the time period encompasses 10+ revolutions of the machine (bearing) being monitored, although fewer revolutions might suffice. The output can be continuous (voltage, current, light) and updated at the end of each pre-set time increment and/or periodic in an automated sense (e.g., level transmitted on a fixed time interval by wireless means) or as interrogated from an external source. The peak g-level obtained from the apparatus of the invention will a) be trended and b) initiate alert/fault levels. The alert/fault levels will have predetermined action levels to be executed by the appropriate groups.
When monitoring the stress wave activity of machinery, healthy machinery will have no or very little stress wave activity (<0.5 g typically), but machinery with faults can have significant g-levels (50 g's is not unusual). Therefore, the dynamic range of a suitable sensing and analysis instrument must be in the 50-100 dB range. For an instrument operating in the linear domain, this dynamic range is not practical without a) scale changing (shift linear range upward or downward through changing gains) or b) defining a lower signal level at which no attempt is made to resolve. An alternative approach, improvement to the sensor, is to switch to a non-linear output relative to input through, for example, employing a logarithmic amplifier which is readily available with a 100 dB dynamic range.
The “peak g-level” value is acquired by selecting the peak value captured over a time interval sufficient to encompass, in a preferred embodiment, a minimum of 6+ revolutions of the machine being monitored (it is necessary to incorporate greater than 2.5 revolutions since some faults reach their peak value only once per 2.5 or so revolutions). It is possible that an occasional observed peak value is bogus (i.e., not machine related). An improvement of the sensor of the present invention is to accommodate these “bogus” readings in such a manner as to maintain the integrity of the sensor output. One means to accomplish this is to collect many “peak values” over short time durations and then select the “peak value” for the desired time increment (sufficient to encompass the desired number of revolutions) based on a statistical analysis to separate out the statistical “outliers”. Another means, in a preferred embodiment, is to establish a “running average” of the “peak values” over the last “N” (“N” can be defined to be any value, but the probable range would be 3-10) values observed. This “running” average can be accomplished through simple electronic means.
Others have patented various inventions related to vibration analysis, but none of these patent teach stress wave detection and analysis as in the present invention.
For instance, U.S. Pat. No. 4,729,239 (Gordon) discloses a vibration tester for ball bearings and associated devices. This patented invention, directed to the testing of a miniature ball bearing, uses an accelerometer to pick up the movement of the bearing and sense the vibration signal therefrom, an integrator to obtain a velocity measurement, and an output to detect any frequencies resulting from a flaw in the miniature bearing.
U.S. Pat. No. 5,381,692 (Winslow et al.) discloses a bearing assembly monitoring system which measures both vibration and temperature in real time.
U.S. Pat. No. 5,511,422 (Hernandez) discloses a personal computer based method and apparatus for analyzing and detecting faults in bearings and other rotating components that slip. The device senses vibration with an accelerometer sensor and shaft encoder, and processes the signals by a series of steps to generate coherently averaged spectra and derived features that indicate defects in, or certain other features, of machine components that slip or rotate asynchronously.
U.S. Pat. No. 5,703,295 (Ishida et al.) discloses a vibration sensing method and apparatus therefor. The invention includes an acceleration sensor for outputting a sensing signal corresponding to vibration, a level discriminator for generating an output when the output level of the accelerometer exceeds a preset reference level, a display unit for displaying the output of the level discriminator, a piezoelectric ceramic power generating unit for generating a charge when it is subjected to vibration, and a conversion unit for converting the charge generated by the piezoelectric ceramic generating unit into DC power for use by the apparatus.
U.S. Pat. No. 5,895,857 (Robinson et al.) discloses a vibration sensor and processing device comprising a peak detector operatively arranged to detect peak amplitude values over a predetermined period of time.
U.S. Pat. No. 6,053,047 (Dister et al.) discloses a diagnostic system for determining faults in multiple bearings using one vibration sensor. The system analyzes vibration signatures about critical frequencies to determine the health of a rotating machine.
U.S. Pat. No. 6,116,089 (El-lbiary et al) discloses a method and apparatus for identifying defects in a rotating machine system. The invention uses a vibration signature comparison method, which stores the vibration signature of a healthy machine in memory and compares it with a vibration signature obtained later to diagnose state of the machine.
U.S. Pat. No. 6,370,957 (Filippenko et al.) discloses yet another vibration analysis method for predictive maintenance of a rotating machine. This invention uses collected vibration data to calculate a set of statistical parameters of vibration such as root mean square (RMS), kurtosis (KU), crest factor (CF), high frequency enveloping (HFE) as well as trending of the mean values of the selected areas of averaged spectra. The combination of the values is used to calculate two general output values characterizing a mechanical condition of the rotating machine.
U.S. Patent Application Publication No. US2001/0042229 (James) discloses a fault monitoring system that includes an integrator that counts in one direction when a fault is detected and counts in an opposite direction in the absence of fault detection. The system enables one to distinguish between intermittent and hard faults.
U.S. Patent Application Publication No. US2002/0139191 (Hedeen et al.) discloses a system and method for conditioned-based monitoring of a bearing assembly. The system comprises a sensor placed in proximity to a bearing assembly, which generates a signal indicative of the amplitude and frequency of the vibrational movement of the bearing assembly. A process in communication with the sensor receives the signal from the sensor and generates spectral data representative of the bearing vibrational movement. A database comprises data representative of an amplitude threshold, for at least one predetermined frequency, characteristic of a bearing fault.
However, while the above-identified devices are operatively arranged to detect and monitor macroscopic vibrations, as discussed previously, other types of vibration can be sensed on the microscopic scale. For example, metal-on-metal impacting, metal fatigue, metal cracking, friction and like create stress wave packets that propagate away from the vibration initiation sight at the speed of sound. Stress wave packets are generally short lived, transient events that last only a few milliseconds; they are typically concentrated in small regions that generate “S” waves. As “S” waves propagate away from the initiation site, “ripples” are created, which traverse the surface of the housing. In many cases the ripples can induce an output in an accelerometer (which responds to absolute motion) or other types of sensor, such as strain gauges, etc. Thus, because both macroscopic motion and microscopic waves can induce an output in an accelerometer, it is only logical to use an accelerometer for stress wave analysis.
Fundamental to stress wave analysis is the fact that stress waves are only present when a fault is present, that is, a fault that generates a stress wave. Hence, the analysis of stress waves can provide valuable insight as to the presence of mechanical faults and/or assist in the determination of the severity of a fault.
Typically, stress wave activity can be separated from the macroscopic vibration by routing the signals output by the vibration sensor (accelerometer) through a high pass filter that is calibrated to reject lower frequency vibration signals. By capturing the levels, such as peak g-levels, of the stress waves (where peak level is peak observed over time, for example, 10+ rotations) and analyzing the output, the status, or health of a machine can be determined, especially in the case of a machine comprising rotating roller bearings.
In view of the above, what is needed then is a method and apparatus for sensing and measuring macroscopic and microscopic vibrations in machines using an accelerometer to create vibration signals, processing the vibration signals with a high pass filter to create filter vibration signals, processing the filtered vibration signals with a logarithmic amplifier to create amplified signals, processing the amplified signals with a sample and hold peak detector to determine peaks of the amplified signals, and, average the peaks of the amplified signals.